درخت حوزه‌های تخصصی

The recent surge of artificial-intelligence (AI) activity has sparked concern that large-scale model training, cloud inference, and data-centre expansion could accelerate national energy demand. We marshal a 21-year annual panel for the United States (2004–2024) that couples multiple AI proxies—technology-stock valuations and a ChatGPT-era dummy—with four aggregate energy series (fossil fuels, nuclear, renewables, total primary energy). A five-stage empirical protocol implemented in Python combines Engle–Granger cointegration testing, higher-order ADF stationarity checks, linear and nonlinear dependence diagnostics (Pearson, Dynamic Time Warping, mutual information), multicollinearity screening (variance-inflation factors), and out-of-sample forecasting with linear regression, decision trees, random forests, and support-vector machines augmented by SHAP explainability. Across all tests we find no evidence that AI developments imprint on national energy use: AI variables cointegrate only with one another, their short-run correlations with energy vanish once trends are removed, their mutual-information scores remain near zero, and their inclusion never improves predictive accuracy beyond a parsimonious macro model driven by GDP, inflation, and population. SHAP rankings confirm that AI features carry negligible explanatory weight relative to conventional fundamentals. We conclude that, to date, AI’s macro-level energy footprint is statistically invisible—any electricity it consumes is either too small or offset by efficiency gains within the wider economy. Policymakers should therefore continue to anchor long-range energy scenarios to established economic drivers while monitoring localised data-centre hotspots that national aggregates obscure.

Financial sanctions have economic consequences for the oil-dependent economies. We examined the impact of financial sanctions on exchange rate fluctuations and macroeconomic variables in Iran. To this end, we employed a new Keynesian DSGE model. The results indicated that with the shock in foreign exchange, production (Y) and imports initially decreased. Oil production has shown a positive reaction initially and a negative reaction in the medium term, and after 7 periods, the effect of the shock has disappeared. The capital stock (K) also decreased initially, and in two periods, it reacted positively. In the tenth period, its effect disappeared, and in the long term, it became partially negative, and its effect disappeared. The inflation rate has decreased initially, and its effect disappeared over time. Consumption decreased, and after five cycles, the reaction became positive and then disappeared. The interest rate increased initially and then decreased, and in the 10th period, the shock effect disappeared. The exchange rate initially decreased and then increased after one period.

One of the most important determinants of bank profitability is the bank's financing strategy. Therefore, banks must continuously innovate to design and implement new financing instruments. Among these, Islamic financial instruments—particularly sukuk—play a significant role. The present study investigates the impact of sukuk on different levels of profitability (low, medium, and high) for 24 Islamic banks and 12 conventional banks, considering additional control variables such as GDP growth and inflation. The analysis covers the period 2003–2023 and specifically examines the role of the 2008 global financial crisis. Using dynamic panel quantile regression, the findings reveal that during the crisis, the expansion of the sukuk market positively influenced the profitability of conventional banks but negatively affected Islamic banks, with the strongest reduction observed among banks with low profitability. In contrast, outside the crisis period, sukuk development shows a mixed effect: negative for both Islamic and conventional banks at low and high levels of profitability, while remaining neutral in the middle quantiles.